This document contains summaries of several papers related to artificial intelligence and neural networks: 1. The first paper discusses using recurrent neural networks to plan robot motions in variable environments. 2. The second paper describes using neural network models to classify brain signals and mind states from EEG data. 3. The third paper proposes using coarticulation composite models in acoustic-phonetic decoding to improve recognition rates beyond phonemes, diphones, and triphones.

1. Planning a robot motion using a simple recurrent network
Michaela Ačová
michaela.acova@fpf.slu.cz
Abstract
Recurrent neural network have a capability to capture time and space in their structure, so they are suitable for modelling
dynamic systems. Such a system could be a robot moving in a space with variable size, obstacles placing etc. Via neural
network we can plan a robot motion considering a current context. In this article we describe experiments with neural
networks which we use for a motion planning of a robot. The scope of experiments is to verify the algorithms practically
with Khepera robots.
pp 1 - 6
Classification of brain signals using neural networks
Gabriela Andrejková, Martin Rázus
Gabriela.Andrejkova@upjs.sk, Martin.Razus@email.cz
Abstract
Models of neural networks were used in a classification of mind states. We used some simple electroencephalograph to
get real data from brain signals of people they follow some sequence of pictures. Training of neural networks was
realized using the data and the trained neural networks were applied in the classification of mind states of tested
respondents. The results give us a starting point to a construction of a simple game which can be driven by signals from
a brain.
pp 7 - 12
Coarticulation composite models in acoustic-phonetic decoding
David Andrš, Kamil Ekštein
andrsd@kiv.zcu.cz
Abstract
The main problem of acoustic-phonetic decoding is to recognize base fonetic units. According to the research in previous
years, the recognition rate could be improved by extending the modeled context of phonetic units. The advancefrom
phonemes to diphones or triphones is remarkable. To obtain reliable models for triphones for the given language means to
create high-quality corpus, which is very tough and expensive work.
This paper describes an alternative method for modeling wider contexts, without the need of gathering all existing
triphones. The method is using the monophones as the base units. They are composed into more complex units composite
models. The next base idea of this method is so called coarticulation effect. The first acoustic-phonetic decoders used
phoneme as the base unit and they ignored the coarticulation effect. Extending the base units to diphones and triphones,
the decoders took the effect of coarticulation into account. This brings a lot of problems with gathering corpus data.
Further in the paper, we describe problems related with the decoding based on coarticulation composite models and we
propose possible solutions for them.
pp 13 - 18

2. Optimization of random forests using the methods of computational intelligence
Peter Angelovič, Vladimír Vojtek
angelovic@fiit.stuba.sk
Abstract
Machine learning methods, which are based on mathematical models, are used in prediction tasks namely regression,
classification and time series prediction. One of these approaches is proposed in this paper. It is modification of
Classification and Regression Tree (CART), which is used to form the collection of predictors. Final prediction is
calculated from partial results of each CART tree. This collection of predictors is called Random Forest. Important
problem in the construction of random forest is the random selection of parameters. The results can be improved with the
evolutionary algorithms and also with the artificial neural networks.
pp 19 - 24
Evolutionary optimization methods in echo state neural networks training
Štefan Babinec
stefan.babinec@stuba.sk
Abstract
“Echo State” neural networks, which are a special case of recurrent neural networks, are studied from the viewpoint of
their learning ability, with a goal to achieve their greater prediction ability. A standard training of these neural networks
uses pseudoinverse matrix for one-step learning of weights from hidden to output neurons. The advantage of one-step
learning changes into a disadvantage, when we want to improve the predictive abilities of the given trained network. In
this approach such learning was substituted by different optimization algorithms - Evolutionary Strategy, Simulated
Annealing and SOMA algorithm. The main goal of this paper was to compare results achieved by optimization
algorithms with results achieved by original one-step learning algorithm. These optimization algorithms were tested in
turbojet gas temperature prediction, and the prediction error was substantially smaller in comparison with prediction error
achieved by a standard training algorithm.
pp 25 - 36
Time series prediction models evaluation
Jozef Babjak
jozef.babjak@stuba.sk
Abstract
There exist a lot of ways how to evaluate time series prediction models. Not all of them are equally appropriate in all
situations. This paper discus about criterions which is needed to consider when time series prediction models are
evaluated and compared. The paper also explains why some commonly used metrics of model success are not able to
compare two different models or evaluate model performance for any time series. Finally, there is suggest of appropriate
metric for time series prediction models evaluation. Suggested criterion is able to evaluate an ability of different models
to predict different time series.
pp 37 - 40

3. Communication networks and phase transitions
Dušan Bernát, Martin Nehéz
bernat@fiit.stuba.sk, nehez@fel.cvut.cz
Abstract
The article deals with various aspects of phase transition phenomenon, known especially from physical processes (like
change of phase, emergence of superconductivity, percolation, etc.). Similar rapid changes in some of properties (e.g.
success of message delivery) can be observed in communication networks, which can be modelled by random graphs. We
give an overview and comparison of methods used to study the properties and behaviour of systems, where the phase
transition occur. Particularly we mention some of the methods used in physics and their algebraic interpretation, the
theory of random graphs and possibilities of their simulation, as well as some known results in the field.
pp 41 - 46
Kurt Gödel—life, results and their significance
Libor Běhounek
behounek@cs.cas.cz
Abstract
Kurt Gödel is often regarded as the most remarkable logician of the XX century, especially for his famous Incom-
pleteness Theorems. Besides the basic biographical and bibliographical data, the paper surveys his most important results
and works that influenced mathematical logic and metamathematics (Completeness and Incompleteness Theorems,
infinite-valuedness of intuitionistic logic), set theory (irrefutability of the continuum hypothesis and the axiom of choice,
axioms for the theory of classes), philosophy of mathematics (the views presented in his essays), and other disciplines (a
peculiar solution to Einstein’s equations allowing time travel, a logical reconstruction of Anselm’s ontological proof). In
particular we focus on the consequences of Gödel’s Incompleteness Theorems for computer science (Turing’s results on
algorithmic undecidability), artificial intelligence, philosophy, and other disciplines. Arguments are given to show why
most applications of the Incompleteness Theorems to philosophy of science or philosophy of mind are inappropriate:
first, the theorems only apply to ideal extrapolations to all natural numbers, while in reality we always have to deal with a
finite part thereof; and second, since the proof of Gödel’s theorems can be formalized in set theory (so a machine can
prove it as well), and since we cannot be sure of the consistency of strong enough theories (like set theory), humans have
no advantage over machines in seeing the truth of Gödel sentences.
pp 47 - 58
Significance of emotions for artificial beings
Michal Bída, Cyril Brom, Rudolf Kadlec
michal.bida@seznam.cz; brom@ksvi.mff.cuni.cz; rudolf.kadlec@gmail.com
Abstract
Although emotions are an essential part of human and very probably also other higher animal cognitive processes, they
are rarely used in computer models of organisms. In this article we will investigate the role of artificial emotions in the
models of organisms. We will introduce some psychological theories explaining the origin and importance of emotions.
Then we will discuss the benefits of artificial emotions from the point of view of computer science. We will illustrate the
proposed ideas in two projects – opponents from a 3D computer game, where emotions are used to increase their
believability, and a virtual mouse, where we research the possible importance of emotions for reinforcement learning.
pp 59 - 66

4. Pogamut - a platform for prototyping bots in unreal tournament
Michal Bída, Ondřej Burkert, Cyril Brom a Jakub Gemrot
michal.bida@seznam.cz, ondra.burkert@seznam.cz, brom@ksvi.mff.cuni.cz, jakub.gemrot@gmail.com
Abstract
In this paper, we describe one of the possible approaches to prototyping artificial intelligence of First Person Shooter
computer game opponents. We will present a platform which allows the easy 3D visualization of a virtual environment
and fast modeling of the behavior of virtual agents done by classical symbolical methods. The platform is based on the
game Unreal Tournament, POSH architecture and the programming language Python. We will also discuss sample
projects that we are currently building on this platform.
pp 67 - 74
Perspectives of studying memory activated out of context of the learned event and
environment
Karel Blahna, Petr Telenský
blahna@biomed.cas.cz
Abstract
Arguably, learning and memory belong among most important aspects of cognition. The information acquired by sensory
inputs is filtered and processed in order to be retained by one or more memory systems (if not forgotten on the way).
Basically, memorized information can be retrieved under either set of circumstances: a in the context or environment, in
which the information has been acquired; b in independent situations, not resembling the original context Most animal
studies dealing with memory (in particular that kind of memory which is reffered to as episodic) were traditionally
restrained to conditions described as "a", as the methodical approaches available for studying the "b" cases were only
suitable for humans (where it has been studied thoroughly). However, several papers from the last decade indicate, that
out-of-context memory activation is in principle opened for studying in animals as well.
pp 75 - 78
RNA-like artificial life system
Marian Bobrík
bobrik@cauldron.sk
Abstract
There are many Artificial life (Alife) systems which simulate replication of programs – „virtual organisms“. Function of
these systems is analogical to „replication-first“ scenarios of origin of life, because, at least at the beginning of a
simulation all what the virtual organisms do is producing new copies of themselves. But there is a difference betweeen
RNA (and similar molecules) which is usually assumed to be the first replicator, these programs lack any secondary or
tertiary structure to which RNA owes its catalytic abilities. Their behavior is determined solely by sequence of program
instructions which is an analogy of primary structure. There are also many models and evolutionary simulations based on
secondary structure computation, but none of them is similar to „classic“ Alife system in which computationally universal
programs replicate. To fill this gap a system was created which joins genotype to phenotype mapping via secondary
structure with an interpretation of secondary structure as a program in an Alife system. Behavior of such system and its
variants show in comparison to „classical“ system with equivalent operators, high evolvability and capability of „digital
abiogenesis“ – spontaneous emergence of replicators among randomly generated programs.

5. pp 79 - 88
Hierarchical generalizing grammars
Tomáš Brandejský
Brandejsky@fd.cvut.cz
Abstract
The paper introduces hierarchical generalizing grammars. They are a special sort of grammars, which are formed by
extension of grammar word space, transformation of content grammar into parametric grammar and by generalizing of
that grammar and by construction of hierarchy of these grammars. Presented novel approach to system modeling by
grammars enables to describe variety of systems by these hierarchical generalizing grammars. These systems are known
especially from the area of artificial intelligence and soft-computing; as production expert systems, qualitative models,
neural networks, genetic programming algorithms and models of associative and metaphorical reasoning. Presented paper
brings construction of hierarchical generalizing grammar, as well as construction of description of above mentioned
systems by these grammars and it discuses their possible impacts on the field of computer science a cognitive science.
pp 89 - 96
Artificial intelligence techniques in strategic computer games
Cyril Brom, Jiří Lukavský
brom@ksvi.mff.cuni.cz; lukavsky@praha.psu.cas.cz
Abstract
During past few years computer games has became a phenomenon, which can't be overlooked. One reason of its
popularity is still improving level of computer driven player. Computer game designers are facing challenging tasks to
create better and more human-like behavior of it's AI, that can entertain any fastidious player for a long time. In our
speech we will define main issues and problems and also it's different solutions in turn-based and real-time strategies,
which is, from the point of view of artificial intelligence, one of the most interesting genre of computer games. We will
introduce turn-based strategy game "8 Kingdoms" and descibe particular algorithms of artificial intelligence used in this
game.
pp 97 - 106
Self-organization and cognition
Jan Burian
burianj@vse.cz
Abstract
This article examines the possibilities of methodical use of the theory of self-organizing and complex systems for the
description of cognitive processes. In the beginning we define the notion of self-organization and self-organizing
cognitive agent. The main topic of the article is analysis of different appropriate formal definitions of complexity as the
main characteristics of self-organization. First we describe simple models of self-organization in the cellular automatons,
the complexity in these models emerge form interactions between localized structures and is characterized by fluctuation
of entropy. Then we analyze the so called neural complexity based on mutual information between parts and the whole
which is hoped to solve the dilemma between specialization and integration in the brain. Finally we analyze different
structures describable by power laws. Power laws indicate scale-less, fractal, structures and represent another
characteristic of complexity. Especially we concern with the scale-less networks of relations in the brain and with self-
organized dynamics of cognitive processes describable in terms of the so called self organized criticality.
pp 107 - 118

6. P colonies
Lucie Ciencialová, Luděk Cienciala
Cecilka@gmail.com, ludek.cienciala@fpf.slu.cz
Abstract
In this paper we present the results achieved in research of P colonies, a biochemically inspired formal model
of a computing device. We show that P colonies with one or two objects inside each agent are computationally complete.
pp 119 - 124
Some possibilities of introducing artificial neural networks in teleinformatic
practice
Igor Černák, Milan Lehotský
Igor.Cernak@fedu.ku.sk; Milan.Lehotsky@fedu.ku.sk
Abstract
The paper presents some possibilities of introducing artificial neural networks in teleinformatic mostly in the efficient
control of the processes and the optimization of the routing of the information flows in the net. It is the new perspective
way of the exploiting of the artificial neural networks for the improving of the performance of the teleinformatic nets.
pp 125 - 128
Training recurrent neural networks – algorithms and implementation
Michal Čerňanský
cernansky@fiit.stuba.sk
Abstract
Unlike feedforward neural networks recurrent neural networks can be used to tasks with time context. The paper deals
with the training of recurrent neural networks using common gradient based approaches but also by more advances
approaches based on Kalman filtration. Implementation details of representation of feedforward and recurrent neural
networks are described as well as the training algorithms in pseudo-language.
pp 129 - 136

7. Esperanto as a meta-language in natural language analysis
Kamil Ekštein a David Andrš
kekstein@kiv.zcu.cz
Abstract
A machine understanding of natural human speech is fundamentally based upon semantic analysis. It is a procedure
which is performed by an intelligent machine to “mine” a meaning out of a speaker’s utterance. In the other words it
builds an instance of a formalism which enables to record and further process relevant pieces of information included in
the utterance. Considering current level of knowledge, the procedure is quite far from being optimal. On the one hand
there is no suitable general formalism available (only few problem-oriented approaches), on the other there are only very
unsystematic and not enough general methods for building its instances. An international artificial language Esperanto,
created in 1880s by Polish physician L. L. Zamenhof, suggests itself surprisingly as an alternative formalism. It is
suitable for recording both syntactic and semantic information included in the utterance. This article introduces some
theoretical explorations on an extraction and storage of semantic information in a form of a language which is at the same
time formal and natural. Advantages and limitations of such an approach are discussed. Examples are given showing how
Esperanto can replace some contemporarily used formalisms.
pp 137 - 142
Self-organization as a driving force of dynamical patterns of activity in the brain
and mind
Igor Farkaš
farkas@fmph.uniba.sk
Abstract
The proposed view on mutual connections between brain and mind seeks an answer in self-organization as a dynamical
process, whose effects are discerned on two levels: on the activity level - via emergence (bottom-up causality), as well as
via top-down direction, and on the level of adaptation of both local and global parameters of the formal system. We offer
a brief overview of evidence from the perspectives of cognitive neuroscience and formal models, that are claimed to
support our view, and we emphasize the top-down effects that often remain unexplained in related models. We do not
treat the question of subjectivity of mental states - this is considered an inherent property of a complex dynamical system
operating on neural representations of entities grounded in the outer world.
pp 143 - 148
Cognitive paradigm of information science
Mirka Grešková
mirka.greskova@fphil.uniba.sk
Abstract
Paper is focused on the evolution and the main bases of cognitive paradigm of information science. From the general
point of view, information science is interested in the wide area of information communication in the society. The
cognitive paradigm is aimed at processual aspects of information search and its components, users and information
organization. The main motivation is to introduce information science and mention problems which are relevant for
several fields and bridge the gap between information science and other disciplines within cognitive science. Paper
analyses information behaviour approach, retrieval interaction models and information systems design.
pp 149 - 158

8. Modeling of socio-economic processes (economistic approach)
Lukáš Grulich, Zdeňka Rábová
grulich@fit.vutbr.cz; rabova@fit.vutbr.cz
Abstract
The paper describes a methodological conception for modeling of socio-economic systems. It will be use in a framework
that we develop. The main features of this conception are: The application of the multi-agent paradigma and pure
economistic approach (economical reductionism). It can be obvious to see by using micro-economical (in the sense of
neo-classic economy) triad Subject-Need-Good(that satisfies the need) as the base of the whole conception. The
framework will be used for simple design of an artificial population and an observation of their structure depending on
properties of both the artificial world and subjects (agents) - the principle of emergence.
pp 159 - 166
Experience of episodic situations: Modalities and liminal cases
Ivan M. Havel
havel@cts.cuni.cz
Abstract
The theme of the study is a reflective analysis of human experience of complex episodic situations. Several modalities of
experience are discussed (temporality, spatiality, scene structure, plot, etc.) and in each case the focus is on liminal
experiences. The motivation for the study is searching for possible contact points between two different approaches in
cognitive science, one based on the first-person perspective (phenomenological orientation) and the other based on the
third-person perspective (typical in science).
pp 167 - 180
Evolution and learning of neural driven agents – A dynamical systems
perspective
Matěj Hoffmann
matej.hoffmann@seznam.cz
Abstract
When developing neural controllers for artificial autonomous agents evolutionary or learning techniques (e.g. genetic
algorithms / Hebbian learning, modifications of backpropagation) can be employed. The neural controllers themselves
and even the whole agent-environment interaction can be viewed as dynamical systems [1]. This framework of R. D.
Beer is brought into play on a concrete robot task: environment, robot’s controller and body, their interaction, and fitness
function are formalized. This perspective helped to clarify the difference between different architectures of the neural
controller, identify the role of the researcher and shed light on the interaction of evolution and learning. Finally, this
perspective also clearly revealed that resulting behaviour is a result of the agent-environment interaction and thus we
should not concentrate on the agent neural controller only.
pp 181 - 188

9. Mutation influence on structure of organisms
Marián Ivančo
marian.ivanco@gmail.com
Abstract
The aim of the article is to examine properties of organisms in asexual evolution depending on various mutation rates.
Organisms, dominating at high mutation rate, are not the fittest ones, but those, to which change in internal structure
causes the lowest decrease of fittness [5]. Organisms evolving at medium mutation rate can temporarily sacrifice some
property in order to acquire other, more beneficial property in future, and consecutively optimalise their structure. Loss of
even one property at low mutation rate would lead to extinction of organism before it could acquire new, more valuable
property. Mutation rate thus actively influences the direction of evolution of organisms in environment.
pp 189 - 196
Robot controller based on LSTM network
Peter Kasan a Michal Čerňanský
peter.kasan@gmail.com; michal.cernansky@fiit.stuba.sk
Abstract
A new type of recurrent neural networks called long short-term memory networks have been proposed recently. They
provide some specific aspects in solving certain types of tasks where the common recurrent neural networks fail. In this
contribution a long short-term memory network is used for a problem of robot navigation a in a simple maze with several
T crossings. The network controls the robot motion and turns the robot left or right on a crossing according to a command
given in the beginning of a long hallway terminated by the crossing.
pp 197 - 204
Colonies of grammars and simple membrane structures
Alica Kelemenová
kelemenova@fpf.slu.cz
Abstract
The paper presents an overview of results on the topic of colonies (of grammars) introduced in 1992. Following parts of
the theory are discussed: sequential and parallel colonies, colonies with point mutation (PM colonies) and colonies of
membrane structures (P colonies). We sumarize typical results on the generative power and hierarchy, solutions of
competence and conflicts in parallel models, influence of the position of agents in PM colonies to the development and
forms and the role of simplicity of components in colonies of membrane structures. Actual trends and results on the topic
are also included.
pp 205 - 212
Quantum game theory
Tomáš Kopf
Tomas.Kopf@math.slu.cz
Abstract
A number of models of natural selection is based on game theory. In the last few years, game theory has been extended to
quantum systems. This paper reviews quantum game theory and discusses its consequences for corresponding models of
evolution.
pp 213 - 214

10. Principles of molecular cognition
Ladislav Kováč
kovacl@fns.uniba.sk
Abstract
Natural life (n-life), as arose on Earth and achieved the present state by natural selection, is a chemical system. Its
dynamics is the dynamics of chemical interactions. In contrast to standard chemical reactions in solution, which are scalar
(not running in space coordinates, but in the coordinates of the chemical composition), biochemical processes are
vectorial. The discovery of the vectoriality of biochemical processes by Peter Mitchell is, with its importance, comparable
to Darwin´s discovery of evolution by natural selection. Standard chemical interactions are nomic, and so necessary and
timeless; biochemical interactions are mainly teleonomic: they are outcomes of natural selection. The teleonomic
biochemical interactions represent molecular recognition. Vectoriality of biochemical processes resides in the properties
of proteins. Proteins exhibit molecular sentience. Sentience, a capacity to exhibit a variety of potential internal states,
selected in evolution, and contingent upon the state of the immediate environment, appears to be the main characteristic
of life.
pp 215 - 222
Structural properties of colonies in terminal derivation mode
Adam Kožaný
Adam.Kozany@fpf.slu.cz
Abstract
There were introduced special classes of languages (Reachable, Unreachable, Alive, Dead, …) in PM-Colonies in order
to study its structural properties. In this paper we study these classes of languages for colonies with t mod deravation. We
point out possible combinations of these language classes and other interesting structural properties of colonies with
t mod derivation.
pp 223 - 228
Mathematical modelling of generalization
Věra Kůrková
vera@cs.cas.cz
Abstract
Generalization has been studied in philosophy at the beginning of the 20th century as eidetic generalization, where it was
understood as a process of imagination of possible cases rather than observation of actual ones. Generalization also
became subject of research in computational intelligence, which aims to develop learning methods for artificial neural
networks guaranteeing that networks can recognize of classify also patterns that were not used for their training. For a
mathematical modelling of such capability, in addition to a sample of correctly processed examples some a priori
knowledge about network function should be assumed. Learning with a generalization capability can be modelled in
terms of regularization, which has been developed for search of stable solutions of tasks from physics. In learning theory,
generalization can be investigated as a certain kind of stability.
pp 229 - 232

11. Cognitive science, artificial intelligence, and theory of bounded rationality
Vladimír Kvasnička
kvasnicka@fiit.stuba.sk
Abstract
The presentation is devoted to discussion of possibilities of cognitive science and artificial intelligence to tackle the
problem of bounded rationality. The term „bounded rationality“ was coined fifty years ago by Herbert Simon in his
seminal book „Models of Men“, where it was used as an expression of our bounded possibilities to solve complex and
difficult problems. Cognitive psychologists Daniel Kahneman (Nobel prize winner for economy in 2002) and Amos
Tversky have applied this approach to a study of behavior of rational economic agents. The main idea of this approach is
that complex problems are solved in real time by simple and effective heuristics, which in many cases are not in
accordance with laws of statistics, theory of probability and logic. By using standard test-bed problems from artificial
intelligence (e. g. Tit-for-Tat strategy of cooperating agents, secretary-selection problem of sequential search, or El Farol
bar problem in dynamically changing environment, etc.), we demonstrate that emerging heuristic rules – strategies are
usually very effective in searching for a solution close to an optimal one.
pp 233 - 252
Reinforcement learning
Peter Lacko
lacko@fiit.stuba.sk
Abstract
The purpose of this article is to give a short overview about reinforcement learning. This approach is interesting mainly,
because during learning phase it don’t require pair question/answer. It is sufficient, when we can judge how well a system
is solving a problem. In this article firstly components of reinforcement learning are described. After that we present
learning approaches – value iteration, Q-learning and adaptation of neural network weights with TD(λ) algorithm.
pp 253 - 258
On generative power of positioned eco-grammar systems
Miroslav Langer
miroslav.langer@fpf.slu.cz
Abstract
Positioned eco-grammar systems (PEG systems, for short) were introduced in [5]. In this paper we compare generative
power of PEG systems with generative power of PM colonies and Turing machine. We extend results mentioned in [5]
and [6]. We also muse on possibilities of extension of generative power of PEG systems..
pp 259 - 264
Pattern recognition using a hybrid approach of neural networks and genetic
algorithms
Milan Lehotský, Igor Černák
Milan.Lehotsky@fedu.ku.sk; Igor.Cernak@fedu.ku.sk
Abstract
The paper presents possibilities of the use of the genetic algorithms for training the neural network for the pattern
recognition. The used genetic algorithm and neural network are described and the results of the simulation are presented.

12. pp 265 - 266
Pedestrian recognition based on AOP
Andrej Lúčny
andy@microstep-mis.com
Abstract
We report about our participation on particular project dedicated for pedestrian recognition organized by DENSO
Research Lab. We deal with 3D scene reconstruction from video made by camera mounted in vehicle. We employ
a specific method of software development – a kind of agent-oriented programming (AOP), which is coming from
subsumption architecture by R. Brooks and which is close to society of mind by M. Minsky as well.
pp 267 - 272
Dynamical reservoir of echo state network
Matej Makula
makula@fiit.stuba.sk
Abstract
Echo state networks contain randomly initialized untrained layer formed of a huge number of units. The training process
consists of simple adaptation of weights between hidden and output layers. Units with untrained weights in the hidden
layer form a huge dynamical reservoir that can be used as a source of potentially interesting dynamics needed for creating
a desired network output. Interesting properties of this untrained part of the network are studied on symbolic sequences
processing tasks. Various possibilities of the reservoir initialization and the reservoir structure are considered in order to
improve the network performance.
pp 273 - 282
Coding in human sound localization neural circuits
Petr Maršálek, Július Štroffek
marsalek@cesnet.cz, julo@matfyz.cz
Abstract
One part of the auditory pathway is the system for the sound localization. This is the only sensory system in human brain,
where the duration of one processing step is comparable to the duration of one signal sample of a digitized sound. All the
other sensory and cognitive systems of the brain are much slower, much more complex and much more parallel,
compared to artificial and communication systems. All sound localization theories are constrained by the physical
properties of the surrounding sound space. The encoding of sound in the first neuronal processing stages reflects these
physical properties. We describe in detail the first group of neurons in these processing stages, which combines inputs
from the two ears - the first binaural neurons. We compare the current theories with our alternative theories of sound
localization. There is still much controversy among current experimental and theoretical studies and this leaves an open
space for alternative theories to try to reconcile different opinions.
pp 283 - 288

13. Neural networks and chromosome recognition
Dušan Medera
medera@neuron.tuke.sk
Abstract
We treat chromosome classification as an appearance-based object recognition problem. Chromosomes of the same class
are usually identical in the biological sense. Each individual chromosome in the spread image is localized, segmented,
rotated and straightened in the sequence. Next the banded profiles are used to extract features and all chromosomes are
classified in the karyotype image. The ability of multi-layer neural networks trained with gradient descent to learn
complex, high-dimensional, non-linear mappings from collection of examples makes them candidates for image
recognition tasks. We introduce new approach for the robust feature extraction and chromosome classification based on
the convolutional neural networks. Using of local receptive fields appears to be effective in cases complete band
information in the input vector is unavailable. Experimental results on the chromosomes datasets show, that convolutional
neural network approach is comparable with current performing methods.
pp 289 - 294
Semantic and episodic memory in relation to computerized processing of natural
language semantics
Roman Mouček
moucek@kiv.zcu.cz
Abstract
A long-term memory is considered as a collection of individual memories including a context dependent memory
(dependent on space and time) - an episodic memory, and a context independent memory - a semantic memory
representing facts, concepts and meanings. These memories have an interesting relation to the modern methods and
proposals of software products and computer systems. This relation can be found not only within modelling of specific
software in the fields of artificial intelligence and processing of natural language but also within modelling of common
software products. Then the main attention is paid to the possibilities of computer processing of natural language
(semantic analysis and interpretation).
pp 295 - 298
Evolution and vortex-fractal structures
Pavel Ošmera
osmera@fme.vutbr.cz
Abstract
We would like to find some plausible unifying mechanisms for the explanation of vortex-fractal systems. This paper is an
attempt to attain a new and profound understanding of the nature’s behavior using a vortex-fractal theory. Scientists try to
explain some phenomena in Nature that have not been explained so far. The aim of this paper is the vortex-fractal
explanation of polarization, the diffraction grating, and an attempt to compare quantum electrodynamics (QED) with the
vortex-fractal description. This new approach can be called physics of vortex structures (FVS). The vortex-fractal theory
could possibly explain what the charge, the photon, the electron, the proton, the neutron, etc actually are.
pp 299 - 308

14. Amorphous computing: A model of a universal computer built from simple
cooperating agents
Lukáš Petrů
Lukas.Petru@seznam.cz
Abstract
Amorphous computing studies computing systems composed of a large number of the same working units (agents) that
are randomly distributed in space. The agents may only use local communication (within a fixed communication radius)
and have a severely restricted memory size. Amorphous computing is inspired by the ways in which the living cells
communicate in multicellular organisms. A concrete implementation of an amorphous computer may take the form of an
ad-hoc wireless network or that of a sensor network. The research aims at developing software and communication
protocols for solving basic network tasks, e.g. directing the information flow through the network to some predefined
base station.
We show that algorithm design can be approached at a more general level. Namely, we show that amorphous computing
systems are programmable, i.e., they possess universal computational power and that effective simulation of known
abstract computing machines (especially of Turing machines) is possible. For our results to hold, an amorphous system is
only required to have a sufficient number of nodes distributed with a sufficient density in the plane.
pp 309 - 314
Decisions in social network
Jiří Pospíchal, Vladimír Kvasnička
pospichal@fiit.stuba.sk; kvasnicka@fiit.stuba.sk
Abstract
The paper treats a problem derived from a famous El Farol bar problem, when a bar has only a certain capacity smaller
than the number of potential customers, and these must decide, whether to go or not to go to the bar. Ideally a number of
actual customers would be equal to the capacity of the bar (the problem is similar to „minority game“ or „Boolean
game“). Unlike the original definition of the El Farol bar problem, here we try to solve the problem under condition, that
the capacity of the bar equals half the number of potential customers, customers do not know past numbers of visitors, but
they can ask their acquaintances, whether they plan to go, and decide opposite to their majority. The decision process can
be carried out for several rounds, while customers can change their decision according to the last planned decision of
their acquaintances. Some customers however can decide fixedly immediately, others with a certain probability only after
several rounds of decisions. Every agent has the same strategy; they differ in their neighborhood and randomly taken
initial decision. We are solving a question, which strategy is suitable for all agents for a given type of the network of
acquaintances. The paper measures an influence of the type of the network (complete, random, local, egalitarian and
aristocratic small world network), the influence of the coefficient of changeability of decision and of the number of
rounds of decision making to a decrease of the difference between ideal and real attendance of the bar. In majority of the
cases our proposed heuristic decreases ten times the mean square error of the difference between capacity of the bar and
the number of actual customers, compared to random decisions.
pp 315 - 328

15. Abstraction, comparision and generalization as a basic operation of humane
thinking
Karel Pstružina
Pstruzin@vse.cz
Abstract
Abstraction, comparision and generalization will be in focus of our contribution. They are a basic operation of humane
thinking. We will to make use Kant’s approach to these operation with special attention on the problem of abstraction.
Several remarks will be devoted to matching between Kant and Husserl on reduction and its role when human thinking
creates concepts.
pp 329 - 334
Parallel grammar systems and emergence
Štěpán Raška
Stepan.Raska@seznam.cz
Abstract
In this paper we will compare the efectivity (with respect to the complexity measure index) of the context-free language by
the classical context-free grammar and its representation by the parallel communicating grammar system, working in the
returning mode.
pp 335 - 342
Visual perception – optical illusions
Ján Rybár
rybar@fmph.uniba.sk
Abstract
Maybe it is a paradox but illusions are product of our intelligence. We seek out and study optical illusions (errors of
perception) for better understanding how we see. Topics of this article are illusions connected with size constancy, shape
constancy, seeing brightness and depth perception.
pp 343 - 346
Team strategy in multi-agent environment
Štefan Sudolský
sudolsky@fiit.stuba.sk
Abstract
This project aims to point out various aspects of artificial intelligence in combination with solving of coordinational,
cooperational and planning problems in the frame of multiagent systems and the assets of artificial intelligence application by
the development of intelligence's robots. The aim is to remain focused on simulation backgrounds from which only one
multiagent system has been selected, robotic soccer RoboCup. The primary aspects selected from this complex system and
from the various problems solved include teamwork, planning, recognition and the estimation of opponents.
pp 347 - 354
Dynamic logic programming and world representation in computer games

16. Jozef Šiška
yoyo@ksp.sk
Abstract
Computer games (especially role-playing and adventure games) offer an exact, coherent and relatively small and simple
world description and are usually built on game engines, which provide scripting capabilities. A common task of game
scripting involves evaluation of the world state in a game, usually to find out whether a player has already completed a task
(game quest). In this paper we describe possibilities of integrating Dynamic Logic Programs (DynLoP) into computer game
engines and the use of such representation in the development of a computer game. Dynamic Logic Programming is one of
many formalism for knowledge representation based on Logic Programs. Its declarative nature allows easier and simpler
queries and quest(task) characterization than current imperative scripting languages used in game engines. Furthermore, the
world of a computer game provides an excellent environment for evaluation of DynLoP and its various semantics.
pp 355 - 362
Discrimination criteria based cognitive semantics
Martin Takáč
takac@ii.fmph.uniba.sk
Abstract
In this paper, we propose and formally define a new semantics suitable for representing concepts in dynamic and open
environments, for cognitive models of language evolution and acquisition, BDI architectures and industrial multi-agent
systems. The semantics is based on perceptually grounded conceptual structures - discrimination criteria enabling
representation of objects, properties, relations, changes and situations in a simple and unified way. We further describe
mechanisms of acquisition and continuous updating of the concepts by sensory-motor and linguistic interactions and analyze
their pragmatic usability.
pp 363 - 372
Biological evolution versus evolutionary systems: comparison of rat and robot
learning in similar dynamic spatial tasks
Petr Telenský, Pavel Jiroutek, Jan Svoboda, Karel Blahna a Jan Bureš
telep@centrum.cz; pavel.jiroutek@seznam.cz
Abstract
Ability to adapt to a continuously changing environment is inherent both to natural and artificial "inteligent systems". Our
paper presents comparison of spatial learning in the living being (rat) and artifitial object (autonomous mobile robot
controled by evolutionary system) using a similarily defined task. Individual chapters are given on rat and robot learning.
pp 373 - 392
Text correction with echo state networks

17. Peter Trebatický
trebaticky@fiit.stuba.sk
Abstract
Much of the effort in building recognition systems is devoted to correct the corrupted sequences. In many optical and
handwriting character recognition systems, the last stage employs natural-language analysis techniques to correct corrupted
sequences. Echo state neural networks provide a new view on the recurrent neural network training. We analyse the
possibility of using these easily trainable networks in the task of correction of the text from King James version of the Bible
and compare its performance with the variable length Markov model approach.
pp 393 - 400
Symbol grounding in context of zero semantic commitment
Michal Vavrečka
vavrecka@fss.muni.cz
Abstract
In this article I overview and analyze recent approachs to symbol grounding problem. After short review of historical
roots and analysis of particular processes or components of grounding I focus on general aspects of this process and
demostrate some problems in broader (and possibly) new context. This part is mainly polemic over zero semantic
commitment (Tadeo, 2005) and its application in this area.
pp 401 - 412
E0L eco-colonies
Šárka Vavrečková
sarka.vavreckova@fpf.slu.cz
Abstract
In this paper we deal with generating power of one type of eco-colonies, E0L eco-colonies. This text engages in
comparison of generative power of E0L eco-colonies with generative power of colonies, eco-grammar systems and
0L eco-colonies.
pp 413 - 420

18. Would you like to be a brain in the vat, or on the role of embodiment and
situatedness in the mental development of a cognitive agent
Jiří Wiedermann
jiri.wiedermann@cs.cas.cz
Abstract
The modern theory of cognitive systems sees these systems as autonomous computational systems, which situate
themselves in the environment with the help of their sensorimotor units. In spite of this especially in the circles of
recursivity theorists one can repeatedly hear an opinion that cognition can still be seen from a classical viewpoint, i.e.,
as a problem of a specific data processing and thus, embodiment is not needed in order to capture the principles of
cognition. We show that such a simplified view neglects one substantial property of cognitive systems - viz. their
active participation in the selection and even generation of the input data. Without this feedback, the system cannot
create its internal world model cognized via its own actions. In order to explain the essence of this problem we will
make use of the computational model of a cognitive system, which was introduced by the author in his previous
works. At the principal level, this model enables to think about functioning of the algorithmic mechanisms of
imitation, communication, language acquisition, thinking, and consciousness.
pp 421 - 428
Cognition in visualization
Ján Žiak
ziak@fiit.stuba.sk
Abstract
Processing of visual information starts at the retina and is being further processed in subsequent parts of the brain.
This process can be divided into lower cognitive functions like detection of edges and recognition of colors, and
higher cognitive functions encompassing memory and logical reasoning. Knowledge of perception and cognition is
essential in understanding the pitfalls of visualization and in designing effective visualizations. This article reviews
the perceptual issues of visualization and shows how knowledge of them can be used in favor of the visualization
process.
pp 429 - 434